SFARI Genes
# !diagnostics off
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(plotlyutils)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally) ; library(ggpubr)
library(Rtsne)
library(ClusterR)
library(DESeq2) ; library(limma)
library(expss)
library(knitr) ; library(kableExtra)Load preprocessed dataset (preprocessing code in 01_data_preprocessing.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
rownames(datExpr) = datGenes$ensembl_gene_id
datMeta = datMeta %>% mutate(ID = paste0('X',description))
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=datGenes$ensembl_gene_id, padj = adj.P.Val, log2FoldChange = logFC) %>%
left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'Others', `gene-score`)) %>%
distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='Others' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj)) %>%
mutate(Group = factor(ifelse(gene.score %in% c('Neuronal','Others'), gene.score, 'SFARI'),
levels = c('SFARI', 'Neuronal', 'Others')))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}
rm(GO_annotations)SFARI Gene list
There are 912 genes with a SFARI score, but to map them to the gene expression dataset we had to map the gene names to their corresponding ensembl IDs
Mapping SFARI Gene names to Ensembl IDs
There are 1116 Ensembl IDs corresponding to the 912 genes in the SFARI Gene dataset
Since a gene can have more than one ensembl ID, there were some one-to-many mappings between a gene name and ensembl IDs, so that’s why we ended up with 1116 rows in the SFARI_genes dataset.
The details about how the genes were annotated with their Ensembl IDs can be found in SecondYear/SFARI/RMarkdowns/get_ensembl_ids_new_SFARI.html
There are 87 genes in the SFARI list without a score, of which 0 don’t have syndromic tag either
Exploratory Analysis
There are 826 SFARI Genes in the expression dataset (~74%)
Of these, only 754 have an assigned score
From now on, we’re only going to focus on these 754 genes with a score
Gene count by SFARI score:
table_info = genes_info %>% apply_labels(`gene-score` = 'SFARI Gene Score', syndromic = 'Syndromic Tag',
Neuronal = 'Neuronal Function', gene.score = 'Gene Score') %>%
mutate(syndromic = as.logical(syndromic), Neuronal = as.logical(Neuronal))
cro(table_info$`gene-score`)|  #Total | |
|---|---|
|  SFARI Gene Score | |
| Â Â Â 1Â | 135 |
| Â Â Â 2Â | 201 |
| Â Â Â 3Â | 418 |
|    Others | 14761 |
|    #Total cases | 15515 |
Gene count by Syndromic tag:
cro(table_info$syndromic)|  #Total | |
|---|---|
|  Syndromic Tag | |
| Â Â Â FALSEÂ | 714 |
| Â Â Â TRUEÂ | 112 |
|    #Total cases | 826 |
GO Neuronal annotations:
1087 genes have neuronal-related annotations
153 of these genes have a SFARI score
cro(table_info$gene.score[genes_info$`gene-score` %in% as.character(c(1:3))],
list(table_info$Neuronal[genes_info$`gene-score` %in% as.character(c(1:3))], total()))|  Neuronal Function |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  Gene Score | ||||
| Â Â Â 1Â | 98 | 37 | Â | 135 |
| Â Â Â 2Â | 158 | 43 | Â | 201 |
| Â Â Â 3Â | 345 | 73 | Â | 418 |
|    #Total cases | 601 | 153 |  | 754 |
rm(table_info)
All SFARI scores together
Gene Expression
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>%
left_join(genes_info, by='ID') %>%
mutate(Group = factor(ifelse(gene.score %in% c('Neuronal','Others'), gene.score, 'SFARI'),
levels = c('SFARI', 'Neuronal', 'Others')))
comparisons = list(c('SFARI','Neuronal'), c('Neuronal','Others'), c('SFARI','Others'))
increase = 1
base = 14.5
pos_y_comparisons = c(1:3*increase + base)
p1 = plot_data %>% ggplot(aes(Group, MeanExpr, fill=Group)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .02) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + ggtitle('Mean Expression Comparison') +
theme_minimal() + theme(legend.position='none')
increase = 0.05
base = 0.3
pos_y_comparisons = c(1:3*increase + base)
p2 = plot_data %>% ggplot(aes(Group, SDExpr, fill=Group)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .01) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
coord_cartesian(ylim= c(0.05, max(pos_y_comparisons))) +
xlab('') + ylab('Standard Deviation') + ggtitle('Standard Deviation Comparison') +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
rm(p1, p2, increase, base, pos_y_comparisons)Log Fold Change
Proportion of over- and under-expressed genes is very similar between groups: approximately half
genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'over-expressed', 'under-expressed')) %>%
group_by(Group, direction) %>% tally(name = 'over_expressed') %>%
filter(direction == 'over-expressed') %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('prop_over_expressed' = round(over_expressed/Total,3)) %>%
dplyr::select(-direction) %>% kable %>% kable_styling(full_width = F)| Group | over_expressed | Total | prop_over_expressed |
|---|---|---|---|
| SFARI | 397 | 754 | 0.527 |
| Neuronal | 529 | 934 | 0.566 |
| Others | 6945 | 13827 | 0.502 |
No significant difference to either group
increase = 0.01
base = 0.19
pos_y_comparisons = c(1:3*increase + base)
plot_data %>% ggplot(aes(Group, abs(log2FoldChange), fill=Group)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons,
tip.length = .005) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
coord_cartesian(ylim= c(0.05, max(pos_y_comparisons))) +
xlab('') + ylab('LFC Magnitude') + ggtitle('LFC Magnitude Comparison') +
theme_minimal() + theme(legend.position='none')
rm(increase, base, pos_y_comparisons)- SFARI Genes, as a group, no longer have less genes with high (positive) LFC than the rest of the genes in the dataset, they even seem to have more
plot_data = genes_info %>% dplyr::select(Group, log2FoldChange) %>%
mutate(quant = cut(log2FoldChange, breaks = quantile(log2FoldChange, probs = seq(0,1,0.05)) %>%
as.vector, labels = FALSE),
value_range = cut(log2FoldChange, breaks = quantile(log2FoldChange, probs=seq(0,1,0.05)) %>%
as.vector)) %>%
filter(Group == 'SFARI') %>% group_by(quant, value_range) %>% tally %>% ungroup %>%
left_join(genes_info %>% dplyr::select(Group, log2FoldChange) %>%
mutate(quant = cut(log2FoldChange, breaks = quantile(log2FoldChange,
probs = seq(0,1,0.05)) %>% as.vector, labels = FALSE)) %>%
group_by(quant) %>% tally(name = 'tot') %>% ungroup) %>% mutate(p = 100*n/tot)
ggplotly(plot_data %>% ggplot(aes(quant, p)) + geom_smooth(color = 'gray', alpha = 0.1) +
geom_bar(stat = 'identity', fill = '#00A4F7', aes(id = value_range)) +
geom_hline(yintercept = 100*mean(genes_info$Group == 'SFARI'), color = 'gray', linetype = 'dotted') +
xlab('Log Fold Change Quantiles') + ylab('% of SFARI Genes in each Quantile') + ggtitle('
Distribution of SFARI Genes in LFC Quantiles') + theme_minimal())data.frame('Quantile' = 1:20, 'LFC Range' = cut(genes_info$log2FoldChange,
breaks = quantile(genes_info$log2FoldChange, probs=seq(0,1,.05)) %>% as.vector) %>% table %>% names) %>%
kable(caption = 'LFC ranges for each quantile') %>% kable_styling(full_width = F)| Quantile | LFC.Range |
|---|---|
| 1 | (-1.08,-0.131] |
| 2 | (-0.131,-0.0927] |
| 3 | (-0.0927,-0.069] |
| 4 | (-0.069,-0.0533] |
| 5 | (-0.0533,-0.0412] |
| 6 | (-0.0412,-0.0312] |
| 7 | (-0.0312,-0.0228] |
| 8 | (-0.0228,-0.0143] |
| 9 | (-0.0143,-0.00639] |
| 10 | (-0.00639,0.00111] |
| 11 | (0.00111,0.00835] |
| 12 | (0.00835,0.0161] |
| 13 | (0.0161,0.0239] |
| 14 | (0.0239,0.0322] |
| 15 | (0.0322,0.042] |
| 16 | (0.042,0.0542] |
| 17 | (0.0542,0.0697] |
| 18 | (0.0697,0.0918] |
| 19 | (0.0918,0.13] |
| 20 | (0.13,0.761] |
Differential Expression
Similar proportions
genes_info %>% group_by(Group, significant) %>% tally(name = 'DE') %>% filter(significant) %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('prop_DE' = round(DE/Total,2)) %>% dplyr::select(-significant) %>%
kable(caption = 'Proportion of DE Genes by Group') %>% kable_styling(full_width = F)| Group | DE | Total | prop_DE |
|---|---|---|---|
| SFARI | 124 | 754 | 0.16 |
| Neuronal | 143 | 934 | 0.15 |
| Others | 2182 | 13827 | 0.16 |
SFARI Genes have a lower percentage of DE genes than the Genes with Neuronal annotations and a slightly higher level to the rest of the genes
lfc_list = seq(1, 1.4, 0.01)
all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$Group == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$Neuronal)))
lfc_counts_all = genes_info %>% filter(Group == 'SFARI') %>% tally %>%
mutate('group'='SFARI', 'n'=as.character(n)) %>%
dplyr::select(group, n) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=-1) %>% dplyr::select(lfc, group, n)
for(lfc in lfc_list){
# Recalculate genes_info with the new threshold (p-values change)
DE_genes = topTable(efit, coef=2, number=Inf, sort.by='none', lfc = log2(lfc)) %>% data.frame %>%
left_join(data.frame('ID' = rownames(datExpr), 'AveExpr' = rowMeans(datExpr)), by = 'AveExpr') %>%
mutate(padj = adj.P.Val, log2FoldChange= logFC) %>%
left_join(genes_info %>% dplyr::select(ID, Neuronal, gene.score, Group), by = 'ID') %>%
filter(padj<0.05 & abs(log2FoldChange)>log2(lfc))
# Calculate counts by groups
all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$Group == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Neuronal)))
lfc_counts = DE_genes %>% filter(Group == 'SFARI') %>% tally %>%
mutate('group'='SFARI', 'n'=as.character(n)) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
# Update lfc_counts_all
lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}
# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>%
left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)
# Calculate percentage of each group remaining
tot_counts = genes_info %>% filter(Group == 'SFARI') %>% tally() %>%
mutate('group'='SFARI', 'tot'=n) %>% dplyr::select(group, tot) %>%
bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$Neuronal)),
data.frame('group' = 'Others', 'tot' = sum(genes_info$Group == 'Others')),
data.frame('group'='All', 'tot'=nrow(genes_info)))
lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>%
left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))
# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>%
mutate(group = factor(group, levels = c('SFARI', 'Neuronal', 'Others'))) %>%
ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() +
scale_color_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
ylab('% of Differentially Expressed Genes') + xlab('Fold Change') +
ggtitle('Effect of filtering thresholds in SFARI Genes') + theme_minimal())Grouping Genes by SFARI Gene Score
Gene Expression
Normalised data
- The higher the SFARI score, the higher the mean expression of the gene: This pattern is not as strong as in the other datasets, Could be because this uses microarray technology, which caps the highest expression counts
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>%
left_join(genes_info, by='ID')
comparisons = list(c('1','2'), c('2','3'), c('3','Neuronal'), c('Neuronal','Others'),
c('1','3'), c('3','Others'), c('2','Neuronal'),
c('1','Neuronal'), c('2','Others'), c('1','Others'))
increase = 1
base = 15.5
pos_y_comparisons = c(rep(base, 4), rep(base + increase, 2), base + 2:5*increase)
p1 = plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .02) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('SFARI Gene Scores') + ylab('Mean Expression') +
theme_minimal() + theme(legend.position='none')
increase = 0.02
base = 0.35
pos_y_comparisons = c(rep(base, 4), rep(base + increase, 2), base + 2:5*increase)
p2 = plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .005) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
coord_cartesian(ylim= c(0.05, max(pos_y_comparisons))) +
xlab('SFARI Gene Scores') + ylab('Standard Deviation') +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
rm(p1, p2, base, increase, pos_y_comparisons)Raw data
Just to corroborate that the relation between sd and SFARI score used to be in the opposite direction before the normalisation: The higher the SFARI score the higher the mean expression and the higher the standard deviation
*There are a lot of outliers, but the plot is interactive so you can zoom in
# Save preprocessed results
datExpr_prep = datExpr
datMeta_prep = datMeta
genes_info_prep = genes_info
load('./../Data/filtered_raw_data.RData')
plot_data = data.frame('ID'=datGenes$ensembl_gene_id, 'MeanExpr'=rowMeans(datExpr),
'SDExpr'=apply(datExpr,1,sd)) %>%
left_join(genes_info %>% mutate(ID = datGenes$ensembl_gene_id), by='ID')
p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + theme_minimal() +
theme(legend.position='none'))
p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) + theme_minimal() +
ggtitle('Mean Expression (left) and SD (right) by SFARI score') +
theme(legend.position='none'))
plotly::subplot(p1, p2, nrows=1)#Return to normalised version of the data
datExpr = datExpr_prep
datMeta = datMeta_prep
genes_info = genes_info_prep
rm(plot_data, p1, p2, datExpr_prep, datMeta_prep, genes_info_prep)Log Fold Change
Log Fold Change Direction
The proportion of over- and under-expressed genes in each SFARI Gene score is not very different to the proportion in the genes iwth Neuronal annotations nor in the rest of the genes (good, something less to worry about)
aux = genes_info %>% dplyr::select(ID, log2FoldChange, gene.score) %>%
left_join(data.frame('ID' = rownames(datExpr), 'meanExpr' = rowMeans(datExpr)), by = 'ID') %>%
dplyr::mutate(direction = ifelse(log2FoldChange>0, 'over-expressed', 'under-expressed'))
plot_data = aux %>% group_by(gene.score, direction) %>% tally(name = 'p') %>%
left_join(aux %>% group_by(gene.score) %>% tally, by = 'gene.score') %>% mutate(p = p/n, y=1)
plot_data %>% ggplot(aes(gene.score, p, fill=direction)) + geom_bar(stat='identity') +
geom_hline(yintercept = mean(plot_data$p[plot_data$direction=='under-expressed']),
linetype = 'dashed', color = 'white') +
ylab('Proportion') + xlab('SFARI Gene Scores') +
ggtitle('Direction of Fold-Change in genes by SFARI Score') + theme_minimal()
rm(aux)Log Fold Change Magnitude
The relation between LFC Magnitude and SFARI Gene Scores is not very strong here, it seems like SFARI Genes with a score of 1 have the highest LFC Magnitude of all scores and genes with a score of 3 the lowest, but the difference is not statistically significant this is the oppositve relation we had seen in the other datasets
Note: For clarity, the plot was truncated removing some outlier values
increase = 0.02
base = 0.21
pos_y_comparisons = c(rep(base, 4), rep(base + increase, 2), base + 2:5*increase)
genes_info %>% ggplot(aes(gene.score, abs(log2FoldChange), fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .005) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
coord_cartesian(ylim = c(0, max(pos_y_comparisons))) +
xlab('SFARI Gene Scores') + ylab('LFC Magnitude') +
theme_minimal() + theme(legend.position='none')
rm(increase, base, pos_y_comparisons)We know that in general there is a negative relation between mean expression and LFC in genes, and we also know that there is a strong relation between SFARI Gene Scores and the mean level of expression of the genes
This could explain the behaviour we found above, but we want to see if, once you control for the level of expression, the SFARI genes continue to have this relation to LFC or if it dissapears. (Being optimistic, perhaps the SFARI genes actually have higher LFC than genes with similar levels of expression, but we can’t see this in the original plot because of the relation between level of expression and LFC)
plot_data = genes_info %>% dplyr::select(ID, log2FoldChange, gene.score, significant) %>%
left_join(data.frame('ID' = datGenes$ensembl_gene_id, 'meanExpr' = rowMeans(datExpr)), by = 'ID') %>%
mutate(alpha = ifelse(gene.score == 'Others' , 0.1, ifelse(gene.score == 'Neuronal', 0.3, 0.7)))
increase = 1
base = 15.5
pos_y_comparisons = c(rep(base, 4), rep(base + increase, 2), base + 2:5*increase)
p1 = plot_data %>% ggplot(aes(gene.score, meanExpr, fill=gene.score)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
stat_compare_means(comparisons = comparisons, label = 'p.signif', method = 't.test',
method.args = list(var.equal = FALSE), label.y = pos_y_comparisons, tip.length = .02) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('SFARI Gene Scores') + ylab('Mean Expression') +
theme_minimal() + theme(legend.position='none')
p2 = plot_data %>% ggplot(aes(meanExpr, abs(log2FoldChange), color = gene.score)) +
geom_point(alpha=plot_data$alpha) + geom_smooth(method='lm', color='#999999') +
ylab('LogFoldChange Magnitude') + xlab('Mean Expression') +
scale_color_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
theme_minimal() + theme(legend.position = 'none')
p2 = ggExtra::ggMarginal(p2, type='density', groupColour = TRUE, size=10)
grid.arrange(p2, p1, ncol=2, widths = c(0.6, 0.4))
rm(p1,p2)In this dataset, there seems to be an inverse relation between level of expression and LFC magnitude than in the other three datasets: The higher the level of expression, the higher the LFC Magnitude
Since both the relations between LFC Magnitude by SFARI Score and LFC Manitude by mean expression are the opposite than in the other datasets, they still give us the same conclusion, the level of expression of a gene seems to be related to their LFC Magnitude and this seems to be affecting the SFARI Genes differently by score depending on their level of expression, but in this case, the higher the SFARI Score, the higher the Magnitude of its LFC
plot_data = data.frame('meanExpr' = rowMeans(datExpr), 'LFC_magnitude' = abs(genes_info$log2FoldChange),
'gene.score' = genes_info$gene.score, 'p' = NA) %>% arrange(meanExpr)
w = 1000
for(i in 1:(nrow(plot_data)-w)){
plot_data$p[i+floor(w/2)] = mean(plot_data$LFC_magnitude[i:(i+w)])
}
aux_data = plot_data %>% filter(!gene.score %in% c('Neuronal','Others')) %>% group_by(gene.score) %>%
dplyr::summarise(mean_by_score = mean(meanExpr)) %>% ungroup %>%
mutate('color' = SFARI_colour_hue(r=1:6)[1:3])
ggplotly(plot_data %>% filter(!is.na(p)) %>% ggplot(aes(meanExpr, p)) + geom_line() +
xlab('Mean Level of Expression') + ylab('Sliding Average of LFC Magnitude') +
geom_vline(data = aux_data, aes(xintercept = mean_by_score), color = aux_data$color) +
ggtitle('Sliding Average of LFC Magnitude by Mean Level of Expression') + theme_minimal())rm(aux_data)Fold-Change Magnitude controlling by level of expression
We want to know what happens to the originally negative relation we found between SFARI Gene scores and lFC magnitude when we control for level of expression.
To do this, I’m going to compare each SFARI Gene with its closest non-SFARI neighbours following these steps:
Select one SFARI gene
Select its neighbours: 100 non-SFARI genes with the most similar mean level of Expression
Standardise the lFC magnitude of each of the neighbours and of the SFARI gene (using the mean and sd of the lFC magnitude of only these 101 genes)
Repeat this for each of the SFARI Genes, saving the standardised lFC magnitudes of all the SFARI genes and all the neighbours
Compare the distribution of this value between these two groups (SFARI and their neighbours)
This plot shows the general idea of steps 1, 2, and 3, selecting a random SFARI gene:
The plot on the left shows the original mean expression and lFC magnitude of the SFARI Gene and its 100 closest neighbours
The plot on the right shows the standardised lFC mangitude of the genes, and the vertical lines represent the value that is going to be recorded for each of this genes to be compared afterwards
n = 100
plot_data = genes_info %>% dplyr::select(ID, log2FoldChange, gene.score) %>%
left_join(data.frame('ID' = datGenes$ensembl_gene_id, 'meanExpr' = rowMeans(datExpr)), by = 'ID')
SFARI_gene = plot_data %>% filter(gene.score %in% c('1','2','3','4','5','6')) %>% sample_n(1) %>%
mutate(d=0, alpha = 1)
nn = plot_data %>% filter(gene.score %in% c('Neuronal','Others')) %>%
mutate(d = abs(meanExpr-SFARI_gene$meanExpr), alpha=0.5) %>% top_n(n=-n, wt = d)
plot_data = rbind(SFARI_gene, nn) %>%
mutate(std_magnitude = (abs(log2FoldChange) - mean(abs(log2FoldChange)))/sd(abs(log2FoldChange)))
p1 = plot_data %>% ggplot(aes(meanExpr, abs(log2FoldChange), color = gene.score)) +
geom_point(alpha = plot_data$alpha) + xlab('Mean Expression') + ylab('Log2 Fold Change Magnitude') +
scale_color_manual(values=SFARI_colour_hue(r=c(as.numeric(SFARI_gene$gene.score),8,7))) +
theme_minimal() + theme(legend.position='none')
p2 = plot_data %>% ggplot(aes(meanExpr, std_magnitude, color = gene.score)) +
geom_point(alpha = plot_data$alpha) +
geom_hline(aes(yintercept = mean(std_magnitude)), linetype = 'dashed', color = '#999999') +
scale_color_manual(values=SFARI_colour_hue(r=c(as.numeric(SFARI_gene$gene.score),8,7))) +
geom_segment(aes(x=SFARI_gene$meanExpr, y=mean(std_magnitude), xend = SFARI_gene$meanExpr,
yend = std_magnitude[1]), alpha = 0.5,
color = SFARI_colour_hue(r=1:8)[as.numeric(SFARI_gene$gene.score)]) +
xlab('Mean Expression') + ylab('Standardised LFC Magnitude') +
theme_minimal() + theme(legend.position='none')
for(i in 1:15){
random_sample = plot_data %>% filter(gene.score != SFARI_gene$gene.score) %>% sample_n(1)
p2 = p2 + geom_segment(x=random_sample$meanExpr, xend = random_sample$meanExpr, y=mean(plot_data$std_magnitude),
yend = random_sample$std_magnitude, alpha = 0.5, color = 'gray')
}
grid.arrange(p1, p2, ncol=2, top='Comparing SFARI Genes with their n closest neighbours by Mean Expression')
cat(paste0('SFARI gene\'s standardised distance to its neigbours\'s LFC magnitude: ',
round(plot_data$std_magnitude[1],4)))## SFARI gene's standardised distance to its neigbours's LFC magnitude: 0.4542rm(p1, p2, SFARI_gene, nn, random_sample, i)As steps 4, and 5, say, we repeat this for all of the SFARI Genes, recording their standardised mangnitude as well as the ones from their neighbours so we can study them all together
Results
SFARI Genes seem to behave in a very similar way to their neighbouring genes
get_std_lfc_magnitudes = function(data_info, SFARI_score, n){
SFARI_genes = data_info %>% filter(gene.score == as.character(SFARI_score))
std_magnitudes = data.frame(gene.score = as.character(), std_magnitude = as.numeric)
for(i in 1:nrow(SFARI_genes)){
SFARI_gene = SFARI_genes[i,]
nn = data_info %>% filter(gene.score %in% c('Neuronal','Others')) %>%
mutate(d = abs(meanExpr-SFARI_gene$meanExpr)) %>% top_n(n=-n, wt = d) %>% dplyr::select(-d)
iter_data = rbind(SFARI_gene, nn) %>%
mutate(std_magnitude = (abs(log2FoldChange) - mean(abs(log2FoldChange)))/sd(abs(log2FoldChange))) %>%
dplyr::select(gene.score, std_magnitude)
std_magnitudes = rbind(std_magnitudes, iter_data)
}
return(std_magnitudes)
}
create_plot_by_SFARI_score = function(score, n) {
std_magnitudes = get_std_lfc_magnitudes(data_info, score, n)
plot = std_magnitudes %>% ggplot(aes(gene.score, std_magnitude)) +
geom_boxplot(aes(fill = gene.score), outlier.colour='#cccccc', outlier.shape='o', outlier.size=3) +
xlab('') + ylab('Standardised LFC Magnitude') +
scale_fill_manual(values=SFARI_colour_hue(r=c(score,8,7))) +
coord_cartesian(ylim = c(min(std_magnitudes$std_magnitude), 3)) +
stat_compare_means(method = 't.test', method.args = list(var.equal = FALSE), label = 'p.signif',
ref.group = as.character(score), label.y = 3) +
theme_minimal() + theme(legend.position = 'none')
return(plot)
}
data_info = genes_info %>% dplyr::select(ID, log2FoldChange, gene.score) %>%
left_join(data.frame('ID' = rownames(datExpr), 'meanExpr' = rowMeans(datExpr)), by = 'ID')
p1 = create_plot_by_SFARI_score(1, n)
p2 = create_plot_by_SFARI_score(2, n)
p3 = create_plot_by_SFARI_score(3, n)
grid.arrange(p1, p2, p3, nrow=1,
top = 'Comparison of LFC Magnitude of SFARI gens and their closest neighbours by Mean Expression')
rm(p1, p2, p3)Differential Expression
The proportion of DE genes for each SFARI Genes quite similar, SFARI Genes with Score 1 have a slightly higher proportion than the other two SFARI Scores
plot_info = genes_info %>% group_by(gene.score, significant) %>% tally(name = 'DE') %>% ungroup %>% ungroup %>%
left_join(genes_info %>% group_by(gene.score) %>% tally(name = 'total') %>%
ungroup, by = 'gene.score') %>% filter(significant) %>%
mutate('perc' = 100*DE/total)
ggplotly(plot_info %>% ggplot(aes(gene.score, perc, fill = gene.score)) + geom_bar(stat='identity') +
xlab('SFARI Gene Score') + ylab('% of DE genes') + theme_minimal() + theme(legend.position = 'none') +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))))table_info = genes_info %>% apply_labels(gene.score = 'SFARI Gene Score',
significant = 'Differentially Expressed')
cro(table_info$gene.score, list(table_info$significant, total()))|  Differentially Expressed |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  SFARI Gene Score | ||||
| Â Â Â 1Â | 112 | 23 | Â | 135 |
| Â Â Â 2Â | 168 | 33 | Â | 201 |
| Â Â Â 3Â | 350 | 68 | Â | 418 |
|    Neuronal | 791 | 143 |  | 934 |
|    Others | 11645 | 2182 |  | 13827 |
|    #Total cases | 13066 | 2449 |  | 15515 |
rm(table_info)In our dataset, the higher the level of expression of a gene, the more likely the gene is to be DE (this can be seen by ordering the genes by level of expression and calculating the proportion of DE Genes using a sliding window). Based one this, the SFARI Scores 1 should have the highest proportion of DE Genes, followed closely by Scores 2 and 3 with a similar proportion, which agrees quite well with what we saw in the plot above
plot_data = data.frame('meanExpr' = rowMeans(datExpr), 'DE' = genes_info$significant,
'gene.score' = genes_info$gene.score, 'p' = NA) %>% arrange(meanExpr)
w = 3000
for(i in 1:(nrow(plot_data)-w)){
plot_data$p[i+floor(w/2)] = mean(plot_data$DE[i:(i+w)])*100
}
aux_data = plot_data %>% filter(!gene.score %in% c('Neuronal','Others')) %>% group_by(gene.score) %>%
dplyr::summarise(mean_by_score = mean(meanExpr)) %>% ungroup %>%
mutate('color' = SFARI_colour_hue(r=1:6)[1:3])
ggplotly(plot_data %>% filter(!is.na(p)) %>% ggplot(aes(meanExpr, p)) + geom_line() +
xlab('Mean Level of Expression') + ylab('Sliding Percentage of DE Genes') +
geom_vline(data = aux_data, aes(xintercept = mean_by_score), color = aux_data$color) +
ggtitle('Sliding Percentage of DE Genes by Mean Level of Expression') + theme_minimal()) rm(aux_data, w, i)Differential Expression controlling by level of expression
We want to see how the different scores in the SFARI Genes compare to other groups of genes with similar levels of expression when studying the proportion of DE genes
To do this, I’m going to compare each SFARI Gene with its closest non-SFARI neighbours following these steps:
Select one SFARI gene
Select its neighbours: 100 non-SFARI genes with the most similar mean level of Expression
Calculate the % of these neighbours that are DE and store this value
Repeat this for all of the genes in a specific SFARI score: We have a distribution of % DE neighbours and a single value indicating the percentage of DE genes in that SFARI score
4.1 Measure how annomalous the value for the SFARI scores is by calculating its distance to the distribution (in standard devitions)
- Repeat this for the other SFARI Gene scores
Notes:
Not very different from the distribution of their neighbours
get_neighbours_DE = function(data_info, SFARI_score, n){
SFARI_genes = data_info %>% filter(gene.score == as.character(SFARI_score))
perc_DE = data.frame(gene.score = as.character(), p_DE = as.numeric)
for(i in 1:nrow(SFARI_genes)){
SFARI_gene = SFARI_genes[i,]
nn = data_info %>% filter(gene.score %in% c('Neuronal','Others')) %>%
mutate(d = abs(meanExpr-SFARI_gene$meanExpr)) %>% top_n(n = -n, wt = d) %>%
group_by(gene.score) %>% summarise(perc_DE = 100*mean(significant)) %>% ungroup
perc_DE = rbind(perc_DE, nn)
}
colnames(perc_DE) = c('gene.score', 'perc_DE')
return(perc_DE)
}
calc_dist = function(SFARI_score, df, group){
SFARI_p = 100*mean(genes_info$significant[genes_info$gene.score==SFARI_score])
mean_nn = df$perc_DE[df$gene.score == group] %>% mean
sd_nn = df$perc_DE[df$gene.score == group] %>% sd
dist = round(abs(SFARI_p-mean_nn)/sd_nn, 2)
return(dist)
}
create_plot_by_SFARI_score = function(score, n) {
perc_DE_nn = get_neighbours_DE(data_info, score, n)
plot = perc_DE_nn %>% ggplot(aes(gene.score, perc_DE, fill = gene.score)) + geom_boxplot() +
xlab('') + ylab('% of DE Genes') +
geom_hline(yintercept = 100*mean(genes_info$significant[genes_info$gene.score==as.character(score)]),
color = SFARI_colour_hue(r=1:6)[score]) +
ggtitle(paste0('Neighbours of SFARI Score ', score,' Genes',
'\n\n Dist to Neuronal: ',calc_dist(as.character(score),perc_DE_nn,'Neuronal'),' SD',
'\n Dist to Others: ', calc_dist(as.character(score), perc_DE_nn, 'Others'),' SD')) +
scale_fill_manual(values=SFARI_colour_hue(r=c(8,7))) + theme_minimal() + theme(legend.position='none')
return(plot)
}
data_info = genes_info %>% dplyr::select(ID, significant, gene.score) %>%
left_join(data.frame('ID' = rownames(datExpr), 'meanExpr' = rowMeans(datExpr)), by = 'ID')
n = 100
p1 = create_plot_by_SFARI_score(1, n)
p2 = create_plot_by_SFARI_score(2, n)
p3 = create_plot_by_SFARI_score(3, n)
grid.arrange(p1, p2, p3, nrow = 1)
rm(p1, p2, p3, get_neighbours_DE, calc_dist, create_plot_by_SFARI_score, n)Effects of modifying filtering threshold by SFARI score
SFARI Genes Score 1 has the highest proportion of DE Genes at the beginning, but when we increase the LFC threshold, it quickly decreases.
Using the null hypothesis \(H_0: lfc=0\), 124/754 SFARI genes are statistically significant (~16%)
This results aren’t very robust given the small number of Differentially Expressed enes in each SFARI group
fc_list = seq(1, 1.1, 0.003)
all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$gene.score == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$Neuronal)))
lfc_counts_all = genes_info %>% group_by(`gene-score`) %>% filter(`gene-score` != 'Others') %>% tally %>%
mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
dplyr::select(group, n) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=-1) %>% dplyr::select(lfc, group, n)
for(lfc in lfc_list){
# Recalculate genes_info with the new threshold (p-values change)
DE_genes = topTable(efit, coef=2, number=Inf, sort.by='none', lfc = log2(lfc)) %>% data.frame %>%
left_join(data.frame('ID' = rownames(datExpr), 'AveExpr' = rowMeans(datExpr)), by = 'AveExpr') %>%
mutate(padj = adj.P.Val, log2FoldChange= logFC) %>%
left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'Others', `gene-score`)) %>%
distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='Others' & Neuronal==1, 'Neuronal', `gene-score`)) %>%
filter(padj<0.05 & abs(log2FoldChange)>log2(lfc))
# Calculate counts by groups
all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$`gene-score` == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Neuronal)))
lfc_counts = DE_genes %>% group_by(`gene-score`) %>% tally %>%
mutate('group'=`gene-score`, 'n'=as.character(n)) %>%
bind_rows(Neuronal_counts, all_counts) %>%
mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
# Update lfc_counts_all
lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}
# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>%
left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)
# Calculate percentage of each group remaining
tot_counts = genes_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='Others') %>%
mutate('group'=`gene-score`, 'tot'=n) %>% dplyr::select(group, tot) %>%
bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$Neuronal)),
data.frame('group'='Others', 'tot'=sum(genes_info$gene.score=='Others'&!genes_info$Neuronal)),
data.frame('group'='All', 'tot'=nrow(genes_info)))
lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>%
left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))
# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>% ggplot(aes(lfc, perc, color=group)) +
geom_point(aes(id=n)) + geom_line() + scale_color_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
ylab('% of Differentially Expressed Genes') + xlab('Fold Change') +
ggtitle('Effect of filtering thresholds by SFARI score') + theme_minimal())rm(fc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts,
lfc_counts_all, Others_counts)Conclusion
The patterns found in Gandal are less clear in this dataset, sometimes not being statistically significant any more
The reason could be that this experiment was done with microarray technology, which puts a cap on the level of expression of highly expressed genes, dampening the bias we had observed in other datasets caused by this
The patterns weren’t as clean or significant in Gupta’s dataset as they were in Gandal and our hypothesis for that was an increased variance in the dataset. To corroborate this I compared this dataset’s variance with Gandal: in general, both datasets share a similar variance except for the genes with the lowest levels of expression, which have a lower variance in this dataset
Voineagu_datExpr = datExpr
Voineagu_datGenes = datGenes
load('./../../../Gandal/AllRegions/Data/preprocessed_data.RData')
Gandal_datExpr = datExpr
plot_data = data.frame('ID' = rownames(Gandal_datExpr), 'Gandal_SD' = rowSdDiffs(Gandal_datExpr),
'MeanExpression' = rowMeans(Gandal_datExpr)) %>%
inner_join(data.frame('ID' = Voineagu_datGenes$ensembl_gene_id,
'Voineagu_SD' = rowSdDiffs(Voineagu_datExpr)), by = 'ID') %>%
left_join(DE_info %>% data.frame %>% mutate(ID = rownames(.)), by = 'ID') %>%
mutate(diff = Gandal_SD-Voineagu_SD, abs_diff = abs(Gandal_SD-Voineagu_SD)) %>%
mutate(std_diff = (diff-mean(diff))/sd(diff), distance = abs((diff-mean(diff))/sd(diff)))
plot_data %>% ggplot(aes(Gandal_SD, Voineagu_SD)) + geom_point(alpha=0.1, aes(color=MeanExpression)) +
geom_abline(slope = 1, intercept = 0, color = 'gray', linetype = 'dashed') +
geom_smooth(alpha = 0.1, color = 'gray') + xlab('Gandal') + ylab('Voineagu') +
coord_fixed() + scale_x_continuous(limits = c(0, max(plot_data$Voineagu_SD))) +
scale_colour_viridis(begin=0.1) + ggtitle('SD Comparison between Datasets') + theme_minimal() 
rm(datExpr, datMeta, datGenes, dds, DE_info, Voineagu_datExpr, Gandal_datExpr, plot_data)Session info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] kableExtra_1.1.0 knitr_1.28
## [3] expss_0.10.2 limma_3.40.6
## [5] DESeq2_1.24.0 SummarizedExperiment_1.14.1
## [7] DelayedArray_0.10.0 BiocParallel_1.18.1
## [9] matrixStats_0.56.0 Biobase_2.44.0
## [11] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
## [13] IRanges_2.18.3 S4Vectors_0.22.1
## [15] BiocGenerics_0.30.0 ClusterR_1.2.1
## [17] gtools_3.8.2 Rtsne_0.15
## [19] ggpubr_0.2.5 magrittr_1.5
## [21] GGally_1.5.0 gridExtra_2.3
## [23] viridis_0.5.1 viridisLite_0.3.0
## [25] RColorBrewer_1.1-2 plotlyutils_0.0.0.9000
## [27] plotly_4.9.2 glue_1.4.1
## [29] reshape2_1.4.4 forcats_0.5.0
## [31] stringr_1.4.0 dplyr_1.0.0
## [33] purrr_0.3.4 readr_1.3.1
## [35] tidyr_1.1.0 tibble_3.0.1
## [37] ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
## [4] plyr_1.8.6 lazyeval_0.2.2 splines_3.6.3
## [7] gmp_0.5-13.6 crosstalk_1.1.0.1 digest_0.6.25
## [10] htmltools_0.4.0 fansi_0.4.1 checkmate_2.0.0
## [13] memoise_1.1.0 cluster_2.1.0 annotate_1.62.0
## [16] modelr_0.1.6 jpeg_0.1-8.1 colorspace_1.4-1
## [19] blob_1.2.1 rvest_0.3.5 haven_2.2.0
## [22] xfun_0.12 crayon_1.3.4 RCurl_1.98-1.2
## [25] jsonlite_1.7.0 genefilter_1.66.0 survival_3.1-12
## [28] gtable_0.3.0 zlibbioc_1.30.0 XVector_0.24.0
## [31] webshot_0.5.2 scales_1.1.1 DBI_1.1.0
## [34] miniUI_0.1.1.1 Rcpp_1.0.4.6 xtable_1.8-4
## [37] htmlTable_1.13.3 foreign_0.8-76 bit_1.1-15.2
## [40] Formula_1.2-3 htmlwidgets_1.5.1 httr_1.4.1
## [43] acepack_1.4.1 ellipsis_0.3.1 pkgconfig_2.0.3
## [46] reshape_0.8.8 XML_3.99-0.3 farver_2.0.3
## [49] nnet_7.3-14 dbplyr_1.4.2 locfit_1.5-9.4
## [52] later_1.0.0 tidyselect_1.1.0 labeling_0.3
## [55] rlang_0.4.6 AnnotationDbi_1.46.1 munsell_0.5.0
## [58] cellranger_1.1.0 tools_3.6.3 cli_2.0.2
## [61] generics_0.0.2 RSQLite_2.2.0 broom_0.5.5
## [64] fastmap_1.0.1 evaluate_0.14 yaml_2.2.1
## [67] bit64_0.9-7 fs_1.4.0 nlme_3.1-147
## [70] mime_0.9 ggExtra_0.9 xml2_1.2.5
## [73] compiler_3.6.3 rstudioapi_0.11 png_0.1-7
## [76] ggsignif_0.6.0 reprex_0.3.0 geneplotter_1.62.0
## [79] stringi_1.4.6 highr_0.8 lattice_0.20-41
## [82] Matrix_1.2-18 vctrs_0.3.1 pillar_1.4.4
## [85] lifecycle_0.2.0 data.table_1.12.8 bitops_1.0-6
## [88] httpuv_1.5.2 R6_2.4.1 latticeExtra_0.6-29
## [91] promises_1.1.0 assertthat_0.2.1 withr_2.2.0
## [94] GenomeInfoDbData_1.2.1 mgcv_1.8-31 hms_0.5.3
## [97] grid_3.6.3 rpart_4.1-15 rmarkdown_2.1
## [100] shiny_1.4.0.2 lubridate_1.7.4 base64enc_0.1-3